{"id":"W2359367203","doi":"","title":"Breeding and Utilization of the PTGMS Line P 88 S in Rice","year":2008,"lang":"en","type":"article","venue":"Seed","topic":"Rice Cultivation and Yield Improvement","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Agronomy; Line (geometry); Environmental science; Mathematics; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000445625,0.00002340315,0.00003366103,0.000002996063,0.00004421275,0.000003120549,0.00003992357,0.00001684137,0.00001492646],"category_scores_gemma":[0.00002910549,0.000006775045,0.000009926323,0.0001639347,0.0000193394,0.000030504,0.00001943316,0.00002257081,6.77912e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000350048,"about_ca_system_score_gemma":0.000001451889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001433464,"about_ca_topic_score_gemma":0.00009731213,"domain_scores_codex":[0.999778,0.00000984004,0.00006866011,0.00004976966,0.00005168479,0.00004207836],"domain_scores_gemma":[0.999911,0.00002219862,0.00002842289,0.00001225979,0.00001682871,0.000009327522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000003226062,0.00002152374,0.1088608,0.000002109396,7.844223e-7,1.130297e-7,0.0002302045,0.000001513134,0.8869175,0.0001558281,0.00002950221,0.003776825],"study_design_scores_gemma":[0.00006795244,0.00003149922,0.9834468,0.000005835037,5.822949e-7,6.955926e-7,0.0002313044,0.0001349394,0.01563933,0.00005553967,0.0003657478,0.00001973374],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979039,0.00002760006,0.000002143002,0.001058779,0.00003108598,0.00006277458,0.000001040053,0.000004947221,0.0009076706],"genre_scores_gemma":[0.9994255,0.00003385705,0.00001100003,0.0001901175,0.00002232,0.000001121298,0.000002009905,1.067579e-7,0.0003139985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.874586,"threshold_uncertainty_score":0.03400532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06636652988672818,"score_gpt":0.2389260067733583,"score_spread":0.1725594768866301,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}